Global Competition for Economic Migration:
Visa Policies and Worker Flows

CEP International Economics Workshops

David Cai
j.cai18@lse.ac.uk

London School of Economics

José Ignacio González
j.i.gonzalez-rojas@lse.ac.uk

London School of Economics

Peter J. Lambert
p.j.lambert@lse.ac.uk

London School of Economics

Warwick University

February 25, 2026

Countries are offering more visa schemes to attract talent

The Question

How do Workers Respond to Visa Policies? How Do Countries Design Them?

  • Do new visa schemes attract more workers?
    • Which characteristics of the Visa scheme matter most to attract talent?
  • How do countries design their visa policies in response to other countries’ policies?
    • Do they compete for talent by offering more attractive visa schemes?
    • What are the welfare consequences of their scheme?

What’s Been Done ➕ What’s Missing

Single country analysis (Brinatti & Guo, 2024; Clemens, 2013; Doran et al., 2022; Prato, 2025)

  • USA’s H-1B visa
  • Focus on IT sector
  • Measure effect on firms’ productivity.
  • One migration corridor.
  • What about other occupations, across the globe countries?

Global Analysis (Di Giovanni et al., 2015)

  • OECD data sets on migration stocks.
  • Visa policy design exogenous from the government’s perspective.
  • How do new players in the migration market affect visa policy design?

This Paper

Measuring Worker Flows and Visa Policies

International Economic Migration Flows Data Set

  1. Economic Migrants: LinkedIn Profiles (Amanzadeh et al., 2024).
  2. Flows: Change of position in LinkedIn profiles.
  3. Measure flows between 150+ countries, across 100+ occupations, from 2010 to 2024.

Global Visa Atlas

  • Panel data on visa policies: Origin-destination-year-occupation-experience-education level.
  • Visa policy design: 50+ characteristics of visa policies (e.g. duration, eligibility criteria, costs, among others. )

How Do Workers Respond to Visa Policies?

Staggered DiD with Gravity Specification (Nagengast & Yotov, 2025)

  • Pilot study:
    • 18 countries.
    • 2010-2024.
    • 10 occupations.
  • Treatment: Change in visa scheme.
  • Outcome: Change in worker flows.
  • Results: 52% increase in worker flows after a change in visa scheme characteristics.

Economic Migration Flows

Revelio 2010-2024

Coverage

  • 150+ countries
  • 100+ occupations
  • ~500 million people.

What is a move?

  • Change of position in LinkedIn profiles.
  • International move: Change of country of employment.

Could LinkedIn measure economic migration?

Migration flows are increasing over time

Correlated with official data sets

High-skilled professionals are the biggest users of LinkedIn

Global Visa Atlas

What is a pathway?

Policy-defined route to enter, stay, work, study, obtain protection, or settle, with its own criteria or lifecycle.

Pathways Age limit? Points test? Time to PR Days/yr Start End Main angle
Business Innovation ✅ ✅ \(\sim\) 4–5y 180 2012 2024 Business
Investor ✅ ✅ \(\sim\) 4–5y 180 2012 2024 Investor
Entrepreneur ✅ ❌ \(\sim\) 4–5y 180 2016 2024 Founder
Significant Investor ❌ ❌ \(\sim\) 4y 40 2012 2024 HNW
Premium Investor ❌ ❌ \(\sim\) 1y 0 2015 2021 Ultra-HNW

Simulate workers choosing between different visa schemes

Agentic Design

  • Embed a persona into LLM to simulate a worker’s decision-making process when choosing between different visa schemes.
  • Reflect the characteristics of a typical economic migrant, including their preferences, constraints, and decision-making processes.
  • Consult official visa policy documents, government websites, and other relevant sources.

What are the contracts (Visas) available to workers?

Research Design

What is the growth of migration after new visa pathways are introduced?

Staggered DiD with Gravity Specification (Nagengast & Yotov, 2025)

\[ Y _ {i jk, t} = \exp \left\{\sum_ {g = q} ^ {T} \sum_ {s = g} ^ {T} \delta_ {g s} D _ {g s} + \pi_ {i, t} + \chi_ {j, t} + \tau_ {i, j} + \theta_ {i i, t} + \gamma_{k,t}\right\} \times \epsilon_ {i j, t} \]

  • Control group: dyads that are never treated.
  • Estimated with Nonlinear ETWFE estimator.
  • Estimand: Average Treatment Effect on the Treated (ATT).
  • Pilot study: Sample of 18 countries, 2010-2024, 10 occupations.

52% increase in worker flows after a change in visa pathway

Migration Elasticities

Estimating Migration Elasticities

How do migrants respond to destination characteristics and visa pathway attributes?

When everyone faces the same choice set

\[ U_{ijt} = \underbrace{X_{jt}\beta}_{\text{destination}} + \underbrace{W_{it}\gamma}_{\text{visa pathway}} + \varepsilon_{ijt} \]

\[ \Pr[Y_{it} = j] = \frac{\exp(X_{jt}\beta + W_{it}\gamma)}{\sum_{k \in J} \exp(X_{kt}\beta + W_{kt}\gamma)} \]

Not everyone is eligible for the same visa pathways!

  • H-1B requires employer sponsorship
  • Student visas require university admission
  • \(\Rightarrow\) Choice sets vary: \(CS^*_i \subset J\)

Separate preference from eligibility

Crawford et al. (2021)

The observed probability decomposes as:

\[ \Pr[Y_i = j \mid \theta, \gamma] = \sum_{c \in \mathcal{C}^*_i} \underbrace{\Pr[Y_i = j \mid CS^*_i = c, \theta]}_{\text{Preference (multinomial logit)}} \times \underbrace{\Pr[CS^*_i = c \mid \gamma]}_{\text{Eligibility}} \]

where \(Z_{ij}\) includes eligibility determinants (education, age, family ties, etc.)

Estimate both: Migration elasticities (\(\beta, \theta\)) and eligibility parameters (\(\gamma\))

Conclusion

Main Contributions

Measurement of visa policies and worker flows

  • Comprehensive panel data set on visa policies at the dyadic level.
  • Use of LinkedIn data to measure worker flows across 150+ countries and 100+ occupations.

Quantify the response of workers to visa policies

  • Staggered DiD with gravity specification.
  • Average treatment effect on the treated: change of visa scheme characteristics ➡️ 52% increase in worker flows

Next Steps

  • Increase the visa policy data set to cover more countries and more detailed characteristics.
  • Model counterfactual visa policy design and its welfare implications.
  • Refine treatment definition to capture the most relevant characteristics of visa schemes for different types of workers.

Thank you!

References

References

Amanzadeh, N., Kermani, A., & McQuade, T. (2024). Return Migration and Human Capital Flows (Working Paper No. 32352). National Bureau of Economic Research. https://doi.org/10.3386/w32352
Brinatti, A., & Guo, X. (2024). Third-Country Effects of U.S. Immigration Policy [Working Paper].
Clemens, M. A. (2013). Why Do Programmers Earn More in Houston than Hyderabad? Evidence from Randomized Processing of US Visas. American Economic Review, 103(3), 198–202. https://doi.org/10.1257/aer.103.3.198
Crawford, G. S., Griffith, R., & Iaria, A. (2021). A survey of preference estimation with unobserved choice set heterogeneity. Journal of Econometrics, 222(1), 4–43. https://doi.org/10.1016/j.jeconom.2020.07.024
Di Giovanni, J., Levchenko, A. A., & Ortega, F. (2015). A Global View of Cross-Border Migration. Journal of the European Economic Association, 13(1), 168–202. https://doi.org/10.1111/jeea.12110
Doran, K., Gelber, A., & Isen, A. (2022). The Effects of High-Skilled Immigration Policy on Firms: Evidence from Visa Lotteries. Journal of Political Economy, 130(10), 2501–2533. https://doi.org/10.1086/720467
Nagengast, A. J., & Yotov, Y. V. (2025). Staggered Difference-in-Differences in Gravity Settings: Revisiting the Effects of Trade Agreements. American Economic Journal: Applied Economics, 17(1), 271–296. https://doi.org/10.1257/app.20230089
Prato, M. (2025). The Global Race for Talent: Brain Drain, Knowledge Transfer, and Growth. Quarterly Journal of Economics, 140(1), 165–238. https://doi.org/10.1093/qje/qjae040